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Record W4221103776 · doi:10.18280/ijsse.120104

The Application of BIM-Based OHSMS Information Systems to Improve Safety Performance

2022· article· en· W4221103776 on OpenAlexvenueno aff
Ayu Herzanita, Yusuf Latief, Fatma Lestari

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersDirektorat Riset dan Pengabdian MasyarakatUniversitas Indonesia
KeywordsPlan (archaeology)Process (computing)Risk analysis (engineering)Information systemEngineering managementOrder (exchange)Process managementEngineeringConstruction engineeringSystems engineeringComputer scienceTransport engineeringBusiness

Abstract

fetched live from OpenAlex

The accidents recorded during university construction projects usually affect the safety performance of the institution. This indicates it is important to recognize the hazards and construction risks at the design stage in order to have an optimal safety plan. Moreover, the university area has several organizations involved in the implementation of Occupational Health and Safety Management Systems (OHSMS). Therefore, there is a need to develop the OHSMS information system to accommodate the role of the parties involved and to disseminate information and communication more effectively. This study was conducted to develop a WBS and BIM-based safety plan for the OHSMS information systems to improve the safety performance using the case study method. The result showed that the WBS is the basic constituent of the safety plan integrated with the BIM to visualize the project in real terms in order to ensure easier identification of hazards and risks. The process involved building data security information in BIM to develop web-based information systems and the BIM-based OHSMS information system developed is expected to improve safety performance in the university area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.339
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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